Sigrid De los Santos
Remove remaining binary file for Hugging Face
9df4cc0
import os
import sys
import argparse
from datetime import datetime
from functools import partial
import datasets
import torch
from torch.utils.tensorboard import SummaryWriter
import wandb
from transformers import (
AutoTokenizer,
AutoModel,
AutoModelForCausalLM,
TrainingArguments,
Trainer,
DataCollatorForSeq2Seq
)
from transformers.trainer import TRAINING_ARGS_NAME
from transformers.integrations import TensorBoardCallback
# Importing LoRA specific modules
from peft import (
TaskType,
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict
)
from utils import *
# Replace with your own api_key and project name
os.environ['WANDB_API_KEY'] = 'ecf1e5e4f47441d46822d38a3249d62e8fc94db4'
os.environ['WANDB_PROJECT'] = 'fingpt-benchmark'
def main(args):
"""
Main function to execute the training script.
:param args: Command line arguments
"""
# Parse the model name and determine if it should be fetched from a remote source
model_name = parse_model_name(args.base_model, args.from_remote)
# Load the pre-trained causal language model
model = AutoModelForCausalLM.from_pretrained(
model_name,
# load_in_8bit=True,
# device_map="auto",
trust_remote_code=True
)
# Print model architecture for the first process in distributed training
if args.local_rank == 0:
print(model)
# Load tokenizer associated with the pre-trained model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# Apply model specific tokenization settings
if args.base_model != 'mpt':
tokenizer.padding_side = "left"
if args.base_model == 'qwen':
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids('<|endoftext|>')
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids('<|extra_0|>')
# Ensure padding token is set correctly
if not tokenizer.pad_token or tokenizer.pad_token_id == tokenizer.eos_token_id:
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model.resize_token_embeddings(len(tokenizer))
# Load training and testing datasets
dataset_list = load_dataset(args.dataset, args.from_remote)
dataset_train = datasets.concatenate_datasets([d['train'] for d in dataset_list]).shuffle(seed=42)
if args.test_dataset:
dataset_list = load_dataset(args.test_dataset, args.from_remote)
dataset_test = datasets.concatenate_datasets([d['test'] for d in dataset_list])
dataset = datasets.DatasetDict({'train': dataset_train, 'test': dataset_test})
# Display first sample from the training dataset
print(dataset['train'][0])
# Filter out samples that exceed the maximum token length and remove unused columns
dataset = dataset.map(partial(tokenize, args, tokenizer))
print('original dataset length: ', len(dataset['train']))
dataset = dataset.filter(lambda x: not x['exceed_max_length'])
print('filtered dataset length: ', len(dataset['train']))
dataset = dataset.remove_columns(['instruction', 'input', 'output', 'exceed_max_length'])
print(dataset['train'][0])
# Create a timestamp for model saving
current_time = datetime.now()
formatted_time = current_time.strftime('%Y%m%d%H%M')
# Set up training arguments
training_args = TrainingArguments(
output_dir=f'finetuned_models/{args.run_name}_{formatted_time}', # 保存位置
logging_steps=args.log_interval,
num_train_epochs=args.num_epochs,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
gradient_accumulation_steps=args.gradient_steps,
dataloader_num_workers=args.num_workers,
learning_rate=args.learning_rate,
warmup_ratio=args.warmup_ratio,
lr_scheduler_type=args.scheduler,
save_steps=args.eval_steps,
eval_steps=args.eval_steps,
fp16=True,
# fp16_full_eval=True,
deepspeed=args.ds_config,
evaluation_strategy=args.evaluation_strategy,
load_best_model_at_end=args.load_best_model,
remove_unused_columns=False,
report_to='wandb',
run_name=args.run_name
)
if not args.base_model == 'mpt':
model.gradient_checkpointing_enable()
model.enable_input_require_grads()
model.is_parallelizable = True
model.model_parallel = True
model.config.use_cache = (
False
)
# model = prepare_model_for_int8_training(model
# setup peft for lora
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
target_modules=lora_module_dict[args.base_model],
bias='none',
)
model = get_peft_model(model, peft_config)
# Initialize TensorBoard for logging
writer = SummaryWriter()
# Initialize the trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
data_collator=DataCollatorForSeq2Seq(
tokenizer, padding=True,
return_tensors="pt"
),
callbacks=[TensorBoardCallback(writer)],
)
# if torch.__version__ >= "2" and sys.platform != "win32":
# model = torch.compile(model)
# Clear CUDA cache and start training
torch.cuda.empty_cache()
trainer.train()
writer.close()
# Save the fine-tuned model
model.save_pretrained(training_args.output_dir)
if __name__ == "__main__":
# Argument parser for command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", default=0, type=int)
parser.add_argument("--run_name", default='local-test', type=str)
parser.add_argument("--dataset", required=True, type=str)
parser.add_argument("--test_dataset", type=str)
parser.add_argument("--base_model", required=True, type=str, choices=['chatglm2', 'llama2', 'llama2-13b', 'llama2-13b-nr', 'baichuan', 'falcon', 'internlm', 'qwen', 'mpt', 'bloom'])
parser.add_argument("--max_length", default=512, type=int)
parser.add_argument("--batch_size", default=4, type=int, help="The train batch size per device")
parser.add_argument("--learning_rate", default=1e-4, type=float, help="The learning rate")
parser.add_argument("--num_epochs", default=8, type=float, help="The training epochs")
parser.add_argument("--gradient_steps", default=8, type=float, help="The gradient accumulation steps")
parser.add_argument("--num_workers", default=8, type=int, help="dataloader workers")
parser.add_argument("--log_interval", default=20, type=int)
parser.add_argument("--warmup_ratio", default=0.05, type=float)
parser.add_argument("--ds_config", default='./config_new.json', type=str)
parser.add_argument("--scheduler", default='linear', type=str)
parser.add_argument("--instruct_template", default='default')
parser.add_argument("--evaluation_strategy", default='steps', type=str)
parser.add_argument("--load_best_model", default='False', type=bool)
parser.add_argument("--eval_steps", default=0.1, type=float)
parser.add_argument("--from_remote", default=False, type=bool)
args = parser.parse_args()
# Login to Weights and Biases
wandb.login()
# Run the main function
main(args)